DocWrangler
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Docwrangler
Overview :
DocWrangler is an open-source interactive development environment designed to simplify the construction and optimization of data processing pipelines based on large language models (LLMs). It provides instant feedback, visualization exploration tools, and AI-assisted features to help users explore data, experiment with various operations, and optimize their pipelines based on findings. Built on the DocETL framework, it is suitable for handling unstructured data, such as text analysis and information extraction. It not only lowers the barrier to LLM data processing but also enhances productivity, enabling users to make more effective use of LLM capabilities.
Target Users :
The target audience includes data scientists, analysts, researchers, and any professionals dealing with large volumes of unstructured data. For beginners, DocWrangler lowers the barrier to entry into LLM data processing; for experienced users, it provides an efficient tool to optimize and accelerate their workflows.
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Use Cases
Analyze common complaints in ICLR 2025 submission reviews.
Process the transcripts of oral arguments from the U.S. Supreme Court in 2024.
Analyze customer support chat logs for airlines to extract key information.
Features
Provides instant feedback and visualization tools to help users quickly iterate and optimize data processing pipelines.
Supports natural language to express data processing requirements without the need for coding or model training.
Equipped with smart hints and automatic visualization features, simplifying data validation and issue detection.
Allows users to provide feedback while reviewing outputs, automatically generating targeted prompt improvement suggestions.
Includes a built-in AI assistant that offers explanations of technical concepts and suggestions for pipeline structure improvements.
How to Use
1. Visit http://docetl.org/playground and upload your data.
2. Set your API key, dataset description, and sample size.
3. Use open-ended prompts to start data exploration and gradually build your pipeline.
4. Review outputs one by one and leverage smart hints for optimization.
5. Use the optimization operation function as needed to handle complex documents or tasks.
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